Goals for this file

Use raw fastq and generate the quality plots to asses the quality of reads
Filter and trim out bad sequences and bases from our sequencing files
Write out fastq files with high quality sequences
Evaluate the quality from our filter and trim.
Infer errors on forward and reverse reads individually
Identified ASVs on forward and reverse reads separately using the error model.
Merge forward and reverse ASVs into “contigous ASVs”.
Generate ASV count table. (otu_table input for phyloseq.).

Output that we need:

ASV count table: otu_table
Sample information: sample_table track the reads lost throughout DADA2 workflow.

Before you start

Set my seed

# Any number can be chose
set.seed(567890)

Load Libraries

#Effecient package loading with pacman
pacman::p_load(tidyverse, devtools, dada2, phyloseq, patchwork, DT,
               install = FALSE)

Load Data

#Set the raw fastq path to the raw sequencing files
#Path to the fastq files
raw_fastqs_path <- "data/01_DADA2/01_raw_gzipped_fastqs/2017"
raw_fastqs_path

## [1] "data/01_DADA2/01_raw_gzipped_fastqs/2017"

#What files are in this path (Intuition check)
list.files(raw_fastqs_path)

##  [1] "SRR17060816_1.fastq.gz" "SRR17060816_2.fastq.gz" "SRR17060817_1.fastq.gz"
##  [4] "SRR17060817_2.fastq.gz" "SRR17060818_1.fastq.gz" "SRR17060818_2.fastq.gz"
##  [7] "SRR17060819_1.fastq.gz" "SRR17060819_2.fastq.gz" "SRR17060820_1.fastq.gz"
## [10] "SRR17060820_2.fastq.gz" "SRR17060821_1.fastq.gz" "SRR17060821_2.fastq.gz"
## [13] "SRR17060824_1.fastq.gz" "SRR17060824_2.fastq.gz" "SRR17060830_1.fastq.gz"
## [16] "SRR17060830_2.fastq.gz" "SRR17060831_1.fastq.gz" "SRR17060831_2.fastq.gz"
## [19] "SRR17060832_1.fastq.gz" "SRR17060832_2.fastq.gz" "SRR17060833_1.fastq.gz"
## [22] "SRR17060833_2.fastq.gz" "SRR17060834_1.fastq.gz" "SRR17060834_2.fastq.gz"
## [25] "SRR17060835_1.fastq.gz" "SRR17060835_2.fastq.gz" "SRR17060836_1.fastq.gz"
## [28] "SRR17060836_2.fastq.gz" "SRR17060837_1.fastq.gz" "SRR17060837_2.fastq.gz"
## [31] "SRR17060838_1.fastq.gz" "SRR17060838_2.fastq.gz" "SRR17060846_1.fastq.gz"
## [34] "SRR17060846_2.fastq.gz" "SRR17060847_1.fastq.gz" "SRR17060847_2.fastq.gz"

#How many files are there?
str(list.files(raw_fastqs_path))

##  chr [1:36] "SRR17060816_1.fastq.gz" "SRR17060816_2.fastq.gz" ...

#Create a vector of forward reads
forward_reads <- list.files(raw_fastqs_path, pattern = "_1.fastq.gz", full.names = TRUE) 
#Intuition check
head(forward_reads)

## [1] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060816_1.fastq.gz"
## [2] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060817_1.fastq.gz"
## [3] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060818_1.fastq.gz"
## [4] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060819_1.fastq.gz"
## [5] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060820_1.fastq.gz"
## [6] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060821_1.fastq.gz"

#Create a vector of reverse reads
reverse_reads <-list.files(raw_fastqs_path, pattern = "_2.fastq.gz", full.names = TRUE)
#Intuition check
head(reverse_reads)

## [1] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060816_2.fastq.gz"
## [2] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060817_2.fastq.gz"
## [3] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060818_2.fastq.gz"
## [4] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060819_2.fastq.gz"
## [5] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060820_2.fastq.gz"
## [6] "data/01_DADA2/01_raw_gzipped_fastqs/2017/SRR17060821_2.fastq.gz"

Raw Quality plots

# Randomly select 12 samples from dataset to evaluate 
# Selecting 12 is typically better than 2 (like we did in class for efficiency)
random_samples <- sample(1:length(reverse_reads), size = 12)
random_samples

##  [1] 16 15  1 14  6 17 11 13  4 10 12 18

# Calculate and plot quality of these two samples
forward_filteredQual_plot_12 <- plotQualityProfile(forward_reads[random_samples]) + 
  labs(title = "Forward Read: Raw Quality")

reverse_filteredQual_plot_12 <- plotQualityProfile(reverse_reads[random_samples]) + 
  labs(title = "Reverse Read: Raw Quality")

# Plot them together with patchwork
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Raw Quality Plots

# Aggregate all QC plots 
# Forward reads
forward_preQC_plot <- 
  plotQualityProfile(forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Pre-QC")

# reverse reads
reverse_preQC_plot <- 
  plotQualityProfile(reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Pre-QC")

preQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_preQC_plot + reverse_preQC_plot

# Show the plot
preQC_aggregate_plot

Prepare a placeholder for filtered reads

# vector of our samples, extract the sample information from our file
samples <- 
  scan("/local/workdir/cab565/git_repos/Alessandro/data/00_cutadapt/2017_samples.txt",
                what = "character")
#Intuition check
head(samples)

## [1] "SRR17060816" "SRR17060817" "SRR17060818" "SRR17060819" "SRR17060820"
## [6] "SRR17060821"

#place filtered reads into filtered_fastqs_path
filtered_fastqs_path <- "data/01_DADA2/02b_2017_filtered_fastqs/"
filtered_fastqs_path

## [1] "data/01_DADA2/02b_2017_filtered_fastqs/"

# create 2 variables : filtered_F, filtered_R
filtered_forward_reads <- 
  file.path(filtered_fastqs_path, paste0(samples, "_R1_filtered.fastq.gz"))

#Intuition check
head(filtered_forward_reads)

## [1] "data/01_DADA2/02b_2017_filtered_fastqs//SRR17060816_R1_filtered.fastq.gz"
## [2] "data/01_DADA2/02b_2017_filtered_fastqs//SRR17060817_R1_filtered.fastq.gz"
## [3] "data/01_DADA2/02b_2017_filtered_fastqs//SRR17060818_R1_filtered.fastq.gz"
## [4] "data/01_DADA2/02b_2017_filtered_fastqs//SRR17060819_R1_filtered.fastq.gz"
## [5] "data/01_DADA2/02b_2017_filtered_fastqs//SRR17060820_R1_filtered.fastq.gz"
## [6] "data/01_DADA2/02b_2017_filtered_fastqs//SRR17060821_R1_filtered.fastq.gz"

length(filtered_forward_reads)

## [1] 18

filtered_reverse_reads <- 
  file.path(filtered_fastqs_path, paste0(samples, "_R2_filtered.fastq.gz"))
#Intuition check
length(filtered_reverse_reads)

## [1] 18

Filter and Trim Reads

Parameters of filter and trim DEPEND ON THE DATASET

maxN = number of N bases. Remove all Ns from the data.
maxEE = quality filtering threshold applied to expected errors. By default, all expected errors. Mar recommends using c(1,1). Here, if there is maxEE expected errors, its okay. If more, throw away sequence.
trimLeft = trim certain number of base pairs on start of each read
truncQ = truncate reads at the first instance of a quality score less than or equal to selected number. Chose 2
rm.phix = remove phi x
compress = make filtered files .gzipped
multithread = multithread

#Assign a vector to filtered reads
#Trim out poor bases
#Write out filtered fastq files
filtered_reads <-
  filterAndTrim(fwd = forward_reads, filt = filtered_forward_reads,
              rev = reverse_reads, filt.rev = filtered_reverse_reads,
              truncLen = c(275,250), trimLeft = c(17,21),
              maxN = 0, maxEE = c(1, 1),truncQ = 2, rm.phix = TRUE,
              compress = TRUE, multithread = 6)
# Primers are V3-V4 
# 341F (5′-CCT ACG GGN GGC WGC AG-3′)     (17 bp) from Herlemann et al. 2011
# 785R (5′-GAC TAC HVG GGT ATC TAA TCC-3′)(21 bp) from Herlemann et al. 2011

Trimmed Quality Plots

# Plot the 12 random samples after QC
forward_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_forward_reads[random_samples]) + 
  labs(title = "Trimmed Forward Read Quality")

reverse_filteredQual_plot_12 <- 
  plotQualityProfile(filtered_reverse_reads[random_samples]) + 
  labs(title = "Trimmed Reverse Read Quality")

# Put the two plots together 
forward_filteredQual_plot_12 + reverse_filteredQual_plot_12

Aggregated Trimmed Plots

# Aggregate all QC plots 
# Forward reads
forward_postQC_plot <- 
  plotQualityProfile(filtered_forward_reads, aggregate = TRUE) + 
  labs(title = "Forward Post-QC")

# reverse reads
reverse_postQC_plot <- 
  plotQualityProfile(filtered_reverse_reads, aggregate = TRUE) + 
  labs(title = "Reverse Post-QC")

postQC_aggregate_plot <- 
  # Plot the forward and reverse together 
  forward_postQC_plot + reverse_postQC_plot

# Show the plot
postQC_aggregate_plot

Stats on read output from `filterAndTrim`

#Make output into dataframe
filtered_df <- as.data.frame(filtered_reads)
head(filtered_df)

##                        reads.in reads.out
## SRR17060816_1.fastq.gz   285558     64825
## SRR17060817_1.fastq.gz   676817    122659
## SRR17060818_1.fastq.gz   591364    125713
## SRR17060819_1.fastq.gz   379452     88479
## SRR17060820_1.fastq.gz   570270    128505
## SRR17060821_1.fastq.gz   556682    133522

# calculate some stats
filtered_df %>%
  reframe(median_reads_in = median(reads.in),
          median_reads_out = median(reads.out),
          median_percent_retained = (median(reads.out)/median(reads.in)))

##   median_reads_in median_reads_out median_percent_retained
## 1        455635.5           117175               0.2571683

About 25 percent of reads are retained when maxEE = 1. Quality control plots look good. For the reverse reads, there are more reads with quality scores lower than 30. They occur after 150 bp.

Error Modeling

Note every sequencing run needs to be run separately! The error model MUST be run separately on each illumina dataset. If you’d like to combine the datasets from multiple sequencing runs, you’ll need to do the exact same filterAndTrim() step AND, very importantly, you’ll need to have the same primer and ASV length expected by the output.

Infer error rates for all possible transitions within purines and pyrimidines (A<>G or C<>T) and transversions between all purine and pyrimidine combinations.

Error model is learned by alternating estimation of the error rates and inference of sample composition until they converge.

Starts with the assumption that the error rates are the maximum (takes the most abundant sequence (“center”) and assumes it’s the only sequence not caused by errors).
Compares the other sequences to the most abundant sequence.
Uses at most 10⁸ nucleotides for the error estimation.
Uses parametric error estimation function of loess fit on the observed error rates.

#Forward reads
error_forward_reads <-
  learnErrors(filtered_forward_reads, multithread = 6)

## 103632408 total bases in 401676 reads from 4 samples will be used for learning the error rates.

#Plot forward reads errors
forward_error_plot <-
  plotErrors(error_forward_reads, nominalQ = TRUE) + 
  labs(title =     "Forward Read Error Model")

#Reverse reads
error_reverse_reads <-
  learnErrors(filtered_reverse_reads, multithread = 6)

## 121411449 total bases in 530181 reads from 5 samples will be used for learning the error rates.

#Plot reverse reads errors
reverse_error_plot <-
  plotErrors(error_reverse_reads, nominalQ = TRUE) +
    labs(title = "Reverse Read Error Model")

#Put the two plots together
forward_error_plot + reverse_error_plot

## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.
## log-10 transformation introduced infinite values.

The points do a pretty good job of matching the black lines.

The error rates for each possible transition (A→C, A→G, …) are shown in the plot above.

Details of the plot: - Points: The observed error rates for each consensus quality score.
- Black line: Estimated error rates after convergence of the machine-learning algorithm.
- Red line: The error rates expected under the nominal definition of the Q-score.

Similar to what is mentioned in the dada2 tutorial: the estimated error rates (black line) are a “reasonably good” fit to the observed rates (points), and the error rates drop with increased quality as expected. We can now infer ASVs!

Infer ASVs

An important note: This process occurs separately on forward and reverse reads! This is quite a different approach from how OTUs are identified in Mothur and also from UCHIME, oligotyping, and other OTU, MED, and ASV approaches.

#Infer forward ASVs
dada_forward <- dada(filtered_forward_reads, 
                     err = error_forward_reads,
                     multithread = 6)

## Sample 1 - 64825 reads in 16052 unique sequences.
## Sample 2 - 122659 reads in 7207 unique sequences.
## Sample 3 - 125713 reads in 11422 unique sequences.
## Sample 4 - 88479 reads in 16058 unique sequences.
## Sample 5 - 128505 reads in 12476 unique sequences.
## Sample 6 - 133522 reads in 10879 unique sequences.
## Sample 7 - 87485 reads in 11547 unique sequences.
## Sample 8 - 7184 reads in 1942 unique sequences.
## Sample 9 - 114844 reads in 15535 unique sequences.
## Sample 10 - 64644 reads in 7629 unique sequences.
## Sample 11 - 120738 reads in 16282 unique sequences.
## Sample 12 - 119506 reads in 24424 unique sequences.
## Sample 13 - 124383 reads in 20645 unique sequences.
## Sample 14 - 126386 reads in 9485 unique sequences.
## Sample 15 - 68948 reads in 15339 unique sequences.
## Sample 16 - 127415 reads in 14252 unique sequences.
## Sample 17 - 98952 reads in 16424 unique sequences.
## Sample 18 - 57123 reads in 8619 unique sequences.

#Infer reverse ASVs
dada_reverse <- dada(filtered_reverse_reads, 
                     err = error_reverse_reads, 
                     multithread = 6)

## Sample 1 - 64825 reads in 16522 unique sequences.
## Sample 2 - 122659 reads in 16535 unique sequences.
## Sample 3 - 125713 reads in 17289 unique sequences.
## Sample 4 - 88479 reads in 25463 unique sequences.
## Sample 5 - 128505 reads in 19784 unique sequences.
## Sample 6 - 133522 reads in 20225 unique sequences.
## Sample 7 - 87485 reads in 17623 unique sequences.
## Sample 8 - 7184 reads in 2608 unique sequences.
## Sample 9 - 114844 reads in 27041 unique sequences.
## Sample 10 - 64644 reads in 13452 unique sequences.
## Sample 11 - 120738 reads in 22866 unique sequences.
## Sample 12 - 119506 reads in 34239 unique sequences.
## Sample 13 - 124383 reads in 33256 unique sequences.
## Sample 14 - 126386 reads in 18040 unique sequences.
## Sample 15 - 68948 reads in 24569 unique sequences.
## Sample 16 - 127415 reads in 21086 unique sequences.
## Sample 17 - 98952 reads in 24415 unique sequences.
## Sample 18 - 57123 reads in 15247 unique sequences.

#Inspect
dada_forward[1]

## $SRR17060816_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 461 sequence variants were inferred from 16052 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[1]

## $SRR17060816_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 326 sequence variants were inferred from 16522 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_forward[12]

## $SRR17060834_R1_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 1736 sequence variants were inferred from 24424 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

dada_reverse[12]

## $SRR17060834_R2_filtered.fastq.gz
## dada-class: object describing DADA2 denoising results
## 1143 sequence variants were inferred from 34239 input unique sequences.
## Key parameters: OMEGA_A = 1e-40, OMEGA_C = 1e-40, BAND_SIZE = 16

Merge Forward and Reverse ASVs

Now, merge the forward and reverse ASVs into contigs.

# merge forward and reverse ASVs
merged_ASVs <- mergePairs(dada_forward, filtered_forward_reads, 
                          dada_reverse, filtered_reverse_reads,
                          verbose = TRUE)

## 61597 paired-reads (in 616 unique pairings) successfully merged out of 63526 (in 1570 pairings) input.

## 120825 paired-reads (in 340 unique pairings) successfully merged out of 121793 (in 666 pairings) input.

## 123854 paired-reads (in 305 unique pairings) successfully merged out of 124930 (in 650 pairings) input.

## 86347 paired-reads (in 636 unique pairings) successfully merged out of 87537 (in 889 pairings) input.

## 126896 paired-reads (in 336 unique pairings) successfully merged out of 127832 (in 537 pairings) input.

## 132053 paired-reads (in 329 unique pairings) successfully merged out of 132889 (in 517 pairings) input.

## 85543 paired-reads (in 380 unique pairings) successfully merged out of 86729 (in 764 pairings) input.

## 6842 paired-reads (in 143 unique pairings) successfully merged out of 7045 (in 170 pairings) input.

## 113686 paired-reads (in 385 unique pairings) successfully merged out of 114554 (in 481 pairings) input.

## 63311 paired-reads (in 367 unique pairings) successfully merged out of 64206 (in 466 pairings) input.

## 118599 paired-reads (in 365 unique pairings) successfully merged out of 119938 (in 612 pairings) input.

## 112894 paired-reads (in 1507 unique pairings) successfully merged out of 116722 (in 2369 pairings) input.

## 121780 paired-reads (in 708 unique pairings) successfully merged out of 123187 (in 1073 pairings) input.

## 125536 paired-reads (in 179 unique pairings) successfully merged out of 126044 (in 270 pairings) input.

## 63829 paired-reads (in 1285 unique pairings) successfully merged out of 66635 (in 1877 pairings) input.

## 125671 paired-reads (in 374 unique pairings) successfully merged out of 126924 (in 635 pairings) input.

## 95893 paired-reads (in 505 unique pairings) successfully merged out of 97746 (in 904 pairings) input.

## 55358 paired-reads (in 258 unique pairings) successfully merged out of 56525 (in 439 pairings) input.

# Evaluate the output 
typeof(merged_ASVs)

## [1] "list"

length(merged_ASVs)

## [1] 18

names(merged_ASVs)

##  [1] "SRR17060816_R1_filtered.fastq.gz" "SRR17060817_R1_filtered.fastq.gz"
##  [3] "SRR17060818_R1_filtered.fastq.gz" "SRR17060819_R1_filtered.fastq.gz"
##  [5] "SRR17060820_R1_filtered.fastq.gz" "SRR17060821_R1_filtered.fastq.gz"
##  [7] "SRR17060824_R1_filtered.fastq.gz" "SRR17060830_R1_filtered.fastq.gz"
##  [9] "SRR17060831_R1_filtered.fastq.gz" "SRR17060832_R1_filtered.fastq.gz"
## [11] "SRR17060833_R1_filtered.fastq.gz" "SRR17060834_R1_filtered.fastq.gz"
## [13] "SRR17060835_R1_filtered.fastq.gz" "SRR17060836_R1_filtered.fastq.gz"
## [15] "SRR17060837_R1_filtered.fastq.gz" "SRR17060838_R1_filtered.fastq.gz"
## [17] "SRR17060846_R1_filtered.fastq.gz" "SRR17060847_R1_filtered.fastq.gz"

# Inspect the merger data.frame from the 20210602-MA-ABB1P 
#head(merged_ASVs[[3]])

Create Raw ASV Count Table

# Create the ASV Count Table 
raw_ASV_table <- makeSequenceTable(merged_ASVs)

# Write out the file to data/01_DADA2


# Check the type and dimensions of the data
dim(raw_ASV_table)

## [1]   18 5510

class(raw_ASV_table)

## [1] "matrix" "array"

typeof(raw_ASV_table)

## [1] "integer"

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table)))

## 
##  258  259  260  261  290  300  326  350  355  372  373  374  382  383  384  385 
##   12   12   43    1    1    2    1    5    1    2    3    1    3    3    4    1 
##  386  389  396  400  401  402  403  404  405  406  407  408  409  410  411  412 
##    4    2    1    2   70  607  294  191  513   67   19   11    8   18    4    9 
##  413  414  415  416  417  418  419  420  421  422  423  424  425  426  427  428 
##    2    9    4    9    9    8    9    9   65 1304   61   42   19  315 1238  443 
##  429  430  431  432  450  451  454  455  460 
##   32    7    1    1    3    1    2    1    1

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Raw distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

###################################################
###################################################
# TRIM THE ASVS
# Let's trim the ASVs to only be the right size, which is 421-429.

# We will allow for a few 
raw_ASV_table_trimmed <- raw_ASV_table[,nchar(colnames(raw_ASV_table)) %in% 421:429]

# Inspect the distribution of sequence lengths of all ASVs in dataset 
table(nchar(getSequences(raw_ASV_table_trimmed)))

## 
##  421  422  423  424  425  426  427  428  429 
##   65 1304   61   42   19  315 1238  443   32

# What proportion is left of the sequences? 
sum(raw_ASV_table_trimmed)/sum(raw_ASV_table)

## [1] 0.8249287

# Inspect the distribution of sequence lengths of all ASVs in dataset 
# AFTER TRIM
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Let's zoom in on the plot 
data.frame(Seq_Length = nchar(getSequences(raw_ASV_table_trimmed))) %>%
  ggplot(aes(x = Seq_Length )) + 
  geom_histogram() + 
  labs(title = "Trimmed distribution of ASV length") + 
  scale_y_continuous(limits = c(0, 500))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_bar()`).

Taking into account the lower, zoomed-in plot. Do we want to remove those extra ASVs? No

Remove Chimeras

Sometimes chimeras arise in our workflow.

Chimeric sequences are artificial sequences formed by the combination of two or more distinct biological sequences. These chimeric sequences can arise during the polymerase chain reaction (PCR) amplification step of the 16S rRNA gene, where fragments from different templates can be erroneously joined together.

Chimera removal is an essential step in the analysis of 16S sequencing data to improve the accuracy of downstream analyses, such as taxonomic assignment and diversity assessment. It helps to avoid the inclusion of misleading or spurious sequences that could lead to incorrect biological interpretations.

# Remove the chimeras in the raw ASV table
noChimeras_ASV_table <- removeBimeraDenovo(raw_ASV_table_trimmed, 
                                           method="consensus", 
                                           multithread=6, verbose=TRUE)

## Identified 1276 bimeras out of 3519 input sequences.

# Check the dimensions
dim(noChimeras_ASV_table)

## [1]   18 2243

# What proportion is left of the sequences? 
sum(noChimeras_ASV_table)/sum(raw_ASV_table_trimmed)

## [1] 0.9561805

sum(noChimeras_ASV_table)/sum(raw_ASV_table)

## [1] 0.7887808

# Plot it 
data.frame(Seq_Length_NoChim = nchar(getSequences(noChimeras_ASV_table))) %>%
  ggplot(aes(x = Seq_Length_NoChim )) + 
  geom_histogram()+ 
  labs(title = "Trimmed + Chimera Removal distribution of ASV length")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Track the read counts

Here, we will look at the number of reads that were lost in the filtering, denoising, merging, and chimera removal.

# A little function to identify number seqs 
getN <- function(x) sum(getUniques(x))

# Make the table to track the seqs 
track <- cbind(filtered_reads, 
               sapply(dada_forward, getN),
               sapply(dada_reverse, getN),
               sapply(merged_ASVs, getN),
               rowSums(noChimeras_ASV_table))

head(track)

##                        reads.in reads.out                            
## SRR17060816_1.fastq.gz   285558     64825  63902  64385  61597  47244
## SRR17060817_1.fastq.gz   676817    122659 122122 122253 120825 114882
## SRR17060818_1.fastq.gz   591364    125713 125166 125385 123854 115030
## SRR17060819_1.fastq.gz   379452     88479  87940  87995  86347  51420
## SRR17060820_1.fastq.gz   570270    128505 128060 128209 126896 107865
## SRR17060821_1.fastq.gz   556682    133522 133151 133213 132053 120482

# Update column names to be more informative (most are missing at the moment!)
colnames(track) <- c("input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")
rownames(track) <- samples

# Generate a dataframe to track the reads through our DADA2 pipeline
track_counts_df <- 
  track %>%
  # make it a dataframe
  as.data.frame() %>%
  rownames_to_column(var = "names") %>%
  mutate(perc_reads_retained = 100 * nochim / input)

# Visualize it in table format 
DT::datatable(track_counts_df)

# Plot it!
track_counts_df %>%
  pivot_longer(input:nochim, names_to = "read_type", values_to = "num_reads") %>%
  mutate(read_type = fct_relevel(read_type, 
                                 "input", "filtered", "denoisedF", "denoisedR", "merged", "nochim")) %>%
  ggplot(aes(x = read_type, y = num_reads, fill = read_type)) + 
  geom_line(aes(group = names), color = "grey") + 
  geom_point(shape = 21, size = 3, alpha = 0.8) + 
  scale_fill_brewer(palette = "Spectral") + 
  labs(x = "Filtering Step", y = "Number of Sequences") + 
  theme_bw()

Prepare the data for export!

1. ASV Table

Below, we will prepare the following:

Two ASV Count tables:
1. With ASV seqs: ASV headers include the entire ASV sequence ~250bps.
2. with ASV names: This includes re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below.
ASV_fastas: A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).

Finalize ASV Count Tables

########### 2. COUNT TABLE ###############
############## Modify the ASV names and then save a fasta file!  ############## 
# Give headers more manageable names
# First pull the ASV sequences
asv_seqs <- colnames(noChimeras_ASV_table)
asv_seqs[1:5]

## [1] "TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAAGGGATGTTGCTAATATCAGCATCCTGTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGCCTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACGGCATCCAAAACTGAGAGGCTCGAGTGCGGAAGAGGAGTGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACACTCTGGTCTGACACTGACGCTGAGGTACGAAAGCGTGGGGAGCAAACA"
## [2] "CCAAGAATATTCCGCAATGGGGGAAACCCTGACGGAGCGACACTGCGTGAATGATGAAGGCCTTCGGGTTGTAAAGTTCTTTTATAAAGGAAGAATAAGTTGGGTAGGAAATGACCTGATGATGACGGTACTTTATGAATAAGTCCCGGCTAATTACGTGCCAGCAGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTCCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCCAAGATTGACGCTGAGGCGCGAAAGTGTGGGGATCAAATA"
## [3] "CCAAGAATATTCCGCAATGGGGGGAACCCTGACGGAGCGACACTGCGTGAATGAAGAAGGCCTTCGGGTTGTAAAGTTCTTTTATAAAGGAAGAATAAGTTATGTAGGAAATGACATAATGATGACGGTACTTTATGAATAAGTCCCGGCTAATTACGTGCCAGCAGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTAGGGCTCAACTCTAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTGAGATTGACGCTGAGGCGCGAAAGTGTGGGGATCAAATA"
## [4] "CCAAGAATATTCCGCAATGGGGGGAACCCTGACGGAGCGACACTGCGTGAATGAAGAAGGCCTTCGGGTTGTAAAGTTCTTTTATAAAGGAAGAATAAGTTAGGTAGGAAATGACCTGATGATGACGGTACTTTATGAATAAGTCCCGGCTAATTACGTGCCAGCAGCCGCGGTAATACGTAAGGGACGAGCGTTATTCGGAATTACTGGGCGTAAAGGGCGTGTAGGCGGTTAATTAGGCTGAGTGTTAAAGACTGGGGCTCAACTTCAGAAGGGCATTCAGAACCGGTTGACTAGAATCTGGTGGAAGGCAACGGAATTTCCCGTGTAGCGGTGGAATGCATAGATATGGGAAGGAACACCAAAGGCGAAGGCAGTTGTCTATGCTAAGATTGACGCTGAGGCGCGAAAGTGTGGGGATCAAATA"
## [5] "TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCAGTGAGGAAGGTAATGTAGTTAATACCTGCATTATTTGACGTTAGCTGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCATGCAGGCGGTTTGTTAAGCAAGATGTGAAAGCCCCGGGCTTAACCTGGGAATCGCATTTTGAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTTCAGGTGTAGCGGTGAAATGCGTAGAGATCTGAAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGATGCGAAAGCGTGGGGAGCAAACG"

# make headers for our ASV seq fasta file, which will be our asv names
asv_headers <- vector(dim(noChimeras_ASV_table)[2], mode = "character")
asv_headers[1:5]

## [1] "" "" "" "" ""

# loop through vector and fill it in with ASV names 
for (i in 1:dim(noChimeras_ASV_table)[2]) {
  asv_headers[i] <- paste(">ASV", i, sep = "_")
}

# intitution check
asv_headers[1:5]

## [1] ">ASV_1" ">ASV_2" ">ASV_3" ">ASV_4" ">ASV_5"

##### Rename ASVs in table then write out our ASV fasta file! 
#View(noChimeras_ASV_table)
asv_tab <- t(noChimeras_ASV_table)
#View(asv_tab)

## Rename our asvs! 
row.names(asv_tab) <- sub(">", "", asv_headers)
#View(asv_tab)

Write `01_DADA2` files

Now, we will write the files! We will write the following to the data/01_DADA2/ folder. We will save both as files that could be submitted as supplements AND as .RData objects for easy loading into the next steps into R.:

ASV_counts.tsv: ASV count table that has ASV names that are re-written and shortened headers like ASV_1, ASV_2, etc, which will match the names in our fasta file below. This will also be saved as data/01_DADA2/06_2017_analysis/ASV_counts.RData.
ASV_counts_withSeqNames.tsv: This is generated with the data object in this file known as noChimeras_ASV_table. ASV headers include the entire ASV sequence ~250bps. In addition, we will save this as a .RData object as data/01_DADA2/06_2017_analysis/noChimeras_ASV_table.RData as we will use this data in analysis/02_PreProcessing.Rmd to assign the taxonomy from the sequence headers.
ASVs.fasta: A fasta file output of the ASV names from ASV_counts.tsv and the sequences from the ASVs in ASV_counts_withSeqNames.tsv. A fasta file that we can use to build a tree for phylogenetic analyses (e.g. phylogenetic alpha diversity metrics or UNIFRAC dissimilarty).
We will also make a copy of ASVs.fasta in data/02_PreProcessing/ to be used for the taxonomy classification in the next step in the workflow.
Write out the taxonomy table
track_read_counts.RData: To track how many reads we lost throughout our workflow that could be used and plotted later. We will add this to the metadata in analysis/02_ProProcessing.Rmd.

# FIRST, we will save our output as regular files, which will be useful later on. 
# Save to regular .tsv file 
# Write BOTH the modified and unmodified ASV tables to a file!
# Write count table with ASV numbered names (e.g. ASV_1, ASV_2, etc)
write.table(asv_tab, "data/01_DADA2/Alessandro_2017/ASV_counts.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write count table with ASV sequence names
write.table(noChimeras_ASV_table, "data/01_DADA2/Alessandro_2017/ASV_counts_withSeqNames.tsv", sep = "\t", quote = FALSE, col.names = NA)
# Write out the fasta file for reference later on for what seq matches what ASV
asv_fasta <- c(rbind(asv_headers, asv_seqs))
# Save to a file!
write(asv_fasta, "data/01_DADA2/Alessandro_2017/ASVs.fasta")

# SECOND, let's save to a RData object 
# Each of these files will be used in the analysis/02_Taxonomic_Assignment
# RData objects are for easy loading :) 
save(noChimeras_ASV_table, file = "data/01_DADA2/Alessandro_2017/noChimeras_ASV_table.RData")
save(asv_tab, file = "data/01_DADA2/Alessandro_2017/ASV_counts.RData")
# And save the track_counts_df a R object, which we will merge with metadata information in the next step of the analysis in nalysis/02_Taxonomic_Assignment. 
save(track_counts_df, file = "data/01_DADA2/Alessandro_2017/track_read_counts.RData")

##Session information

#Ensure reproducibility
devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.3.2 (2023-10-31)
##  os       Rocky Linux 9.0 (Blue Onyx)
##  system   x86_64, linux-gnu
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2024-04-25
##  pandoc   3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
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Infer ASVs with DADA2 - 2017 samples

Calla Bush St George

2024-04-25

Goals for this file

Output that we need:

Before you start

Set my seed

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from `filterAndTrim`

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

Write `01_DADA2` files

Infer ASVs with DADA2 - 2017 samples

Calla Bush St George

2024-04-25

Goals for this file

Output that we need:

Before you start

Set my seed

Load Libraries

Load Data

Raw Quality plots

Aggregated Raw Quality Plots

Prepare a placeholder for filtered reads

Filter and Trim Reads

Trimmed Quality Plots

Aggregated Trimmed Plots

Stats on read output from filterAndTrim

Error Modeling

Infer ASVs

Merge Forward and Reverse ASVs

Create Raw ASV Count Table

Remove Chimeras

Track the read counts

Prepare the data for export!

1. ASV Table

Finalize ASV Count Tables

Write 01_DADA2 files

Stats on read output from `filterAndTrim`

Write `01_DADA2` files